Body-worn system for continuous, noninvasive measurement of vital signs

ABSTRACT

The invention provides methods and systems for continuous noninvasive measurement of vital signs such as blood pressure (cNIBP) based on pulse arrival time (PAT). The invention uses a body-worn monitor that recursively determines an estimated PEP for use in correcting PAT measurements by detecting low frequency vibrations created during a cardiac cycle, and a state estimator algorithm to identify signals indicative of aortic valve opening in those measured vibrations.

The present invention is a continuation of U.S. patent application Ser.No. 15/117,162, filed on Aug. 5, 2016, now U.S. Pat. No. 10,856,742,which was filed under 35 U.S.C. § 371 as the U.S. national phase ofInternational Application No. PCT/US2015/014915, filed on Feb. 6, 2015,which designated the U.S. and claims priority to U.S. ProvisionalApplication No. 61/936,850, filed on Feb. 6, 2014, each of which ishereby incorporated in its entirety including all tables, figures, andclaims.

BACKGROUND OF THE INVENTION

The following discussion of the background of the invention is merelyprovided to aid the reader in understanding the invention and is notadmitted to describe or constitute prior art to the present invention.

Pulse oximeters are medical devices featuring an optical module,typically worn on a patient's finger or ear lobe, and a processingmodule that analyzes data generated by the optical module. The opticalmodule typically includes first and second light sources (e.g.,light-emitting diodes, or LEDs) that transmit optical radiation at,respectively, red (λ⁻600-700 nm) and infrared (λ⁻800-1200 nm)wavelengths. The optical module also features a photodetector thatdetects transmitted radiation that passes through an underlying arterywithin, e.g., the patient's finger or earlobe. Typically the red andinfrared LEDs sequentially emit radiation that is partially absorbed byblood flowing in the artery. The photodetector is synchronized with theLEDs to detect the transmitted radiation. In response, the photodetectorgenerates a separate radiation-induced signal corresponding to eachwavelength. The signal, called a plethysmograph, varies in atime-dependent manner as each heartbeat varies the volume of arterialblood and hence the amount of radiation absorbed along the path of lightbetween the LEDs and the photodetector. A microprocessor in the pulseoximeter digitizes and processes plethysmographs generated by the redand infrared radiation to determine the degree of oxygen saturation inthe patient's blood using algorithms known in the art. A number between94%-100% is considered normal, while a number below 85% typicallyindicates the patient requires hospitalization. In addition, themicroprocessor analyzes time-dependent features in the plethysmograph todetermine the patient's heart rate.

Another medical device called an electrocardiograph features conductiveelectrodes, placed at various locations on a patient's body, thatmeasure electrical signals which pass into an amplification circuit. Thecircuit generates a waveform called an electrocardiogram, or ECG, thatdescribes a time-dependent response of the patient's cardiovascularsystem.

Various methods have been disclosed for using both plethysmographs andECGs, taken alone or in combination, to measure blood pressure. Pulsewave velocity defined as the velocity of a pressure pulse launched by aheartbeat in a patient's arterial system, has been shown in a number ofstudies to correlate to both systolic (SYS), diastolic (DIA), and meanblood pressures. In these studies, a surrogate for pulse wave velocityknown as pulse arrival time (PAT) is typically measured with aconventional vital signs monitor that includes separate modules todetermine both an electrocardiogram (ECG waveform) and a value for pulseoximetry (SpO2). During a PAT measurement, multiple electrodes typicallyattach to a patient's chest to determine a time-dependent component ofthe ECG waveform characterized by a sharp spike called the ‘QRScomplex’. The QRS complex indicates an initial depolarization ofventricles within the heart and, informally, marks the beginning of theheartbeat and a pressure pulse that follows. SpO2 is typically measuredwith a bandage or clothespin-shaped sensor that attaches to a patient'sfinger, and includes optical systems operating in both red and infraredspectral regions. A photodetector measures radiation emitted from theoptical systems that transmits through the patient's finger. Other bodysites, e.g., the ear, forehead, and nose, can also be used in place ofthe finger. During a measurement, a microprocessor analyses both red andinfrared radiation measured by the photodetector to determinetime-dependent waveforms corresponding to the different wavelengthscalled photoplethysmographs (PPG waveforms). From these a SpO2 value iscalculated. Time-dependent features of the PPG waveform indicate bothpulse rate and a volumetric absorbance change in an underlying artery(e.g., in the finger) caused by the propagating pressure pulse.

Typical PAT measurements determine the time separating a maximum pointon the QRS complex (indicating the peak of ventricular depolarization)and a portion of the PPG waveform (indicating the arrival of thepressure pulse). PAT depends primarily on arterial compliance, thepropagation distance of the pressure pulse (which is closelyapproximated by the patient's arm length), and blood pressure. Toaccount for patient-specific properties, such as arterial compliance,PAT-based measurements of blood pressure are typically ‘calibrated’using a conventional blood pressure cuff. Typically during thecalibration process the blood pressure cuff is applied to the patient,used to make one or more blood pressure measurements, and then removed.Going forward, the calibration measurements are used, along with achange in PAT, to determine the patient's blood pressure and bloodpressure variability. PAT typically relates inversely to blood pressure,i.e., a decrease in PAT indicates an increase in blood pressure.

A number of U.S. Patents and patent applications describe therelationship between PAT and blood pressure. For example, U.S. Pat. Nos.5,316,008; 5,857,975; 5,865,755; and 5,649,543 each describe anapparatus that includes conventional sensors that measure ECG and PPGwaveforms, which are then processed to determine PAT. PAT has beenidentified as promising surrogate for comfortable quasi-continuous andnon-invasive BP monitoring [1-3]. Calibration steps are typicallynecessary to successfully estimate absolute BP from PAT, which have tobe robust in terms of significant changes of the cardio-vascular status.

While the most common embodiment for measuring PAT is based on asimultaneous detection of an ECG waveform and a PPG waveform measuredfrom the periphery, the measured time difference is the sum of the truevascular transit time (VTT), i.e. the time interval required for thepulse to propagate from the heart to the PPG sensor location, and thepre-ejection period (PEP), which is not related to pulse propagation.Proença et al., 32^(nd) Ann. Intl Conf. of the IEEE EMBS, 598-601,describe a study of the relation of PTT to BP measured by two differentmethods during physical exercise of healthy young subjects. One methodis based on subtracting PEP from PAT, where PEP is provided fromimpedance cardiography. The second approach derives a VTT using two PPGsensors with one sensor positioned at the earlobe and the other at afinger. The results were said to suggest that neither method was good atmonitoring blood pressure changes during exertion, and suggested thatobtaining PEP through the ICG provides great uncertainties which havestrong impact on VTT estimation, and that a PEP-free VTT derived byusing a two PPG setup exhibited a poor correlation to SBP.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide methods and systemsfor continuous noninvasive measurement of vital signs such as bloodpressure (cNIBP) based on PAT, which features a number of improvementsover conventional PAT measurements. The invention uses a body-wornmonitor that recursively determines an estimated PEP for use incorrecting PAT measurements by detecting low frequency vibrationscreated during a cardiac cycle, and using a state estimator algorithm toidentify signals indicative of aortic valve opening in those measuredvibrations. An uncorrected PAT is determined conventionally from theonset of the cardiac cycle and the time at which the correspondingpressure pulse is identified using pulse oximetry. PEP is thendetermined for each cardiac cycle on a beat-to-beat basis based on thedifference between onset of the cardiac cycle (determined from an ECGsensor) and the currently estimated time of aortic valve opening. Usingthese values, a cNIBP measurement is obtained following correction ofthe PAT for PEP. Various vital signs obtained from such a body-wornsystem of sensors may be transmitted to a remote monitor, such as atablet PC, workstation at a nursing station, personal digital assistant(PDA), or cellular telephone.

In a first aspect, the invention relates to methods of obtaining acontinuous measurement of cardiac pre-ejection period for an individualfor a plurality of cardiac cycles occurring over a time n, where ncomprises a plurality of contractions c of the individual's heart. Thesemethods comprise:

-   -   acquiring a time-dependent electrocardiogram waveform using a        first body-worn sensor apparatus configured to detect signals        indicative of electrical activity of the individual's heart for        time n;    -   acquiring a time-dependent vibration waveform using a second        body-worn sensor apparatus configured to detect signals        indicative of vibrations having a frequency between 5 and 35 Hz        caused by compression waves produced due to contraction of the        individual's heart for time n;    -   transmitting the time-dependent electrocardiogram waveform and        the time-dependent vibration waveform to a processing apparatus;        and    -   processing the time-dependent electrocardiogram waveform and the        time-dependent vibration waveform with the processing apparatus        by        -   (i) time synchronizing the time-dependent electrocardiogram            waveform and the time-dependent vibration waveform,        -   (ii) recursively determining an estimated time of aortic            valve opening for each contraction c of the individual's            heart during time n by processing the time-dependent            vibration waveform using a state estimator algorithm which            segments the time-dependent vibration waveform into a moving            time window of length l comprising a plurality of            contractions of the individual's heart which includes            contraction c, and calculates the estimated time of aortic            valve opening for contraction c from the data within the            pre-determined moving window, and        -   (iii) recursively determining a pre-ejection period (PEP)            for each contraction c of the individual's heart during time            n by determining a time difference between a fiducial point            in the time-dependent electrocardiogram waveform indicating            the onset of electrical stimulation of the ventricles during            contraction c and the estimated time of aortic valve opening            determined for contraction c.

The phrase “time synchronizing” as used herein with regard to multiplesensor nodes, each generating a time-dependent waveform, refers tocorrelating the data from each node to a common timing clock. See, e.g.,Elson et al., Fine-Grained Network Time Syncrhonization using ReferenceBroadcasts. In Proc. 5th Symp. Op. Syst. Design Implementation (OSDI),Boston, Mass., 2002; Elson and Romer. Wireless Sensor Networks: A NewRegime for Time Synchronization. In Proc. First Workshop on Hot TopicsIn Networks (HotNets-I), Princeton, N.J., 2002.

The phrase “state estimator algorithm” as used herein refers to analgorithm that uses a series of measurements observed over time,containing noise (random variations) and other inaccuracies, andproduces estimates of unknown variables that tend to be more precisethan those based on a single measurement alone. By way of example only,a Kalman filter operates recursively on streams of noisy input data toproduce a statistically optimal estimate of the underlying system state.In the prediction step, the Kalman filter produces estimates of thecurrent state variables, along with their uncertainties. Once theoutcome of the next measurement (necessarily corrupted with some amountof error, including random noise) is observed, these estimates areupdated using a weighted average, with more weight being given toestimates with higher certainty. Because of the algorithm's recursivenature, it can run in real time using only the present inputmeasurements and the previously calculated state and its uncertaintymatrix; no additional past information is required. This is not meant tobe limiting, as other state estimator algorithms are known in the artwhich may find use in the present invention. These include, but are notlimited to, a boxcar filter, a Wiener filter, a minimum mean squareerror (MMSE) estimator, a recursive least squares estimator, a doubleexponential smoothing estimator, and/or a multi-fractional orderestimator.

The phrase “fiducial point” refers to repeating landmarks within awaveform indicative of a recurring event. In addition to aortic valveopening, other fiducial points in the cardiac cycle include aortic valveclosure, mitral valve closure, etc., and the time of each of theseevents may be extracted from the waveforms recorded in the presentmethods. As described hereinafter, compression waves produced due tocontraction of the individual's heart may be recorded as low frequency(between 5 and 35 Hz) vibrations using sensors which detect acousticand/or accelerometric sensors. In the case of accelerometry, suchmethods are known as seismocardiography, and the waveform is known as aballistocardiogram or a seismocardiogram. See, e.g., Castiglioni et al.,Conf Proc IEEE Eng Med Biol Soc. 2007; 2007:3954-7.

As described herein, the present methods are preferably configured tooperate continually over a time n, where n comprises a plurality ofcardiac cycles. For purposes of this invention, such a method isreferred to as “continuous” operation. In preferred embodiments, n is atleast 15 minutes, preferably at least 30 minutes, more preferably atleast one hour, and most preferably at least 4 hours or more.

As also described herein, the present invention uses a sliding window oftime to divide the time-dependent vibration waveform into a moving timewindow of length 1 comprising a plurality of contractions of theindividual's heart. In preferred embodiments, this pre-determined movingwindow is between 15 seconds and 2 minutes. As described hereinafter,window length 1 may vary over time, such that when input data exhibits alow variance, the window is shortened, and when input data exhibits ahigher variance, the window is lengthened.

Many of the waveforms used in the present claims are sensitive to motionor have a poor signal-to-noise ratio. Thus, the estimator algorithm actsto recursively provide an average measurement of PEP, a PAT, etc., overa time m; for a particular cardiac cycle, the currently measured averagePEP, PAT, etc., may be used as the value of that particular parameterfor the particular cardiac cycle. By way of example, a cardiac cycle mayoccur every 1 second, and an average PEP may be calculated over 10cardiac cycles and updated every 0.5 seconds. At the time of anyparticular cardiac cycle, the average PEP at that instant may be used asthe PEP form that cycle. In preferred embodiments, an averagepre-ejection period (m_(PEP)) is calculated every m seconds from thePEPs determined for each contraction c during a time window wimmediately preceding calculating m_(PEP), wherein m is between 1 and 10seconds, and w is between 15 seconds and 3 minutes.

Additionally, because the waveforms used in the present claims aresensitive to motion or have a poor signal-to-noise ratio, signal metricsmay be utilized to discard particularly corrupted signal time windows.By way of example only, a median of the PEPs (MED_(PEP)) and a varianceof the PEPs (σ² _(PEP)) within time window w may be calculated, and PEPsdetermined for each contraction c during time window w which differ fromMED_(PEP) by more than 1σ, more preferably 2σ or more, may be discardedprior to calculating m_(PEP).

Because measured PAT is the sum of the true PAT (referred to herein asvascular transit time or VTT) and the PEP, PEP (either a single PEPmeasurement or more preferably average PEP (m_(PEP)) can be used toderive the VTT interval on a beat-by-beat basis. In certain embodiments,the present methods comprise acquiring a time-dependent plethysmogramwaveform using a third body-worn sensor apparatus configured to detectsignals indicative of changes in blood volume at an extremity produceddue to contraction of the individual's heart for time n; andtransmitting the time-dependent plethysmogram waveform to the processingapparatus, in order to provide a measured PAT.

In certain embodiments, the processing steps of the present methodsfurther comprise:

-   -   (iv) time synchronizing the time-dependent plethysmogram        waveform with the time-dependent electrocardiogram waveform and        the time-dependent vibration waveform, and    -   (v) recursively determining a Pulse Arrival Time (PAT) for each        contraction c of the individual's heart during time n by        determining a time difference between a fiducial point in the        time-dependent electrocardiogram waveform indicating the onset        of electrical stimulation of the ventricles during contraction c        and a fiducial point in the time-dependent plethysmogram        waveform indicating arrival of the pressure wave at the        extremity due to contraction c.        In certain embodiments, the processing further comprises        recursively calculating an average PAT (m_(PAT)) every p seconds        from the PATs determined for each contraction c during a time        window q, wherein p is between 1 and 10 seconds, and q is        between 15 seconds and 3 minutes; and a median of the PATs        (MED_(PAT)) and a variance of the PATs (σ² _(PAT)) within time        window w may be calculated, and PATs determined for each        contraction c during time window q which differ from MED_(PEP)        by more than 1σ, more preferably 2σ or more, may be discarded        prior to calculating m_(VTT).

For convenience, m=p and w=q; however, these parameters need not beequal when, for example, σ² _(PEP) is substantially different than σ²_(PEP).

In various embodiments, the processing steps further comprisecalculating a vascular transit time (VTT) for each contraction c duringtime n by subtracting m_(PEP) at time t from m_(PAT) at time t., andoptionally calculating a blood pressure value using the VTT.

In a related aspect, the present invention relates to a system forobtaining a continuous measurement of cardiac pre-ejection period for anindividual for a plurality of cardiac cycles occurring over a time n,where n comprises a plurality of contractions c of the individual'sheart. These systems comprise:

-   -   a first body-worn sensor apparatus configured to be worn on the        patient's body and detect signals indicative of electrical        activity of the individual's heart for time n and generate        therefrom a time-dependent electrocardiogram waveform;    -   a second body-worn sensor apparatus configured to be worn on the        patient's body and detect signals indicative of vibrations        having a frequency between 5 and 35 Hz caused by compression        waves produced due to contraction of the individual's heart for        time n and generate therefrom a time-dependent vibration        waveform; and    -   a processing apparatus operably connected to the first body-worn        sensor and the second body-worn sensor, the processing apparatus        configured to receive the time-dependent electrocardiogram        waveform and the time-dependent vibration waveform, and to        -   (i) time synchronize the time-dependent electrocardiogram            waveform and the time-dependent vibration waveform,        -   (ii) recursively determine an estimated time of aortic valve            opening for each contraction c of the individual's heart            during time n by processing the time-dependent vibration            waveform using a state estimator algorithm which segments            the time-dependent vibration waveform into a moving time            window of length l comprising a plurality of contractions of            the individual's heart which includes contraction c, and            calculates the estimated time of aortic valve opening for            contraction c from the data within the pre-determined moving            window, and        -   (iii) recursively determine a pre-ejection period (PEP) for            each contraction c of the individual's heart during time n            by determining a time difference between a fiducial point in            the time-dependent electrocardiogram waveform indicating the            onset of electrical stimulation of the ventricles during            contraction c and the estimated time of aortic valve opening            determined for contraction c.

In certain embodiments, the state estimator algorithm comprises a Kalmanfilter, a boxcar filter, a Wiener filter, a recursive least squaresestimator, a double exponential smoothing estimator, a minimum meansquare error (MMSE) estimator, and/or a multi-fractional orderestimator. As noted above, this list is not meant to be limiting.

In certain embodiments the processing apparatus is further configured torecursively determine an estimated time of aortic valve closure for eachcontraction c of the individual's heart during time n using the stateestimator algorithm.

In certain embodiments the processing apparatus is further configured torecursively determine an estimated time of mitral valve closure for eachcontraction c of the individual's heart during time n using the stateestimator algorithm.

In certain embodiments the processing apparatus is further configured torecursively calculate an average pre-ejection period (m_(PEP)) every mseconds from the PEPs determined for each contraction c during a timewindow w immediately preceding calculating m_(PEP), wherein m is between1 and 10 seconds, and w is between 15 seconds and 3 minutes.

In certain embodiments a median of the PEPs (MED_(PEP)) and a varianceof the PEPs (σ² _(PEP)) within time window w are calculated by theprocessing system, and PEPs determined for each contraction c duringtime window w which differ from PEP_(MED) by more than 2σ are discardedprior to calculating m_(PEP).

In certain embodiments the system further comprises a third body-wornsensor apparatus configured to be worn on the patient's body and detectsignals indicative of changes in blood volume at an extremity produceddue to contraction of the individual's heart for time n and generatetherefrom a time-dependent plethysmogram waveform. In preferredembodiments the third body-worn sensor apparatus is configured to beworn at the base of the patient's thumb or finger. In such embodimentsthe processing apparatus is preferably operably connected to the thirdbody-worn sensor and is configured to

-   -   (iv) time synchronize the time-dependent plethysmogram waveform        with the time-dependent electrocardiogram waveform and the        time-dependent vibration waveform, and    -   (v) recursively determine a Pulse Arrival Time (PAT) for each        contraction c of the individual's heart during time n by        determining a time difference between a fiducial point in the        time-dependent electrocardiogram waveform indicating the onset        of electrical stimulation of the ventricles during contraction c        and a fiducial point in the time-dependent plethysmogram        waveform indicating arrival of the pressure wave at the        extremity due to contraction c.

In certain embodiments the processing system is further configured torecursively calculate an average PAT (m_(PAT)) every p seconds from thePATs determined for each contraction c during a time window q, wherein pis between 1 and 10 seconds, and q is between 15 seconds and 3 minutes.

In certain embodiments a median of the PATs (MED_(P)A_(T)) and avariance of the PATs (σ² _(PAT)) within time window q are calculated bythe processing system, and PATs determined for each contraction c duringtime window q which differ from PAT_(MED) by more than 2σ are discardedprior to calculating m_(VTT).

In certain embodiments the processing system is further configured tocalculate a vascular transit time (VTT) for each contraction c duringtime n by subtracting m_(PEP) at time t from m_(PAT) at time t, andoptionally to calculate a blood pressure value using the VTT.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show an exemplary embodiment of the continuous vitalsign measurement system of the present invention without (FIG. 1A) andwith (FIG. 1B) a cuff-based oscillometric NIBP module.

FIG. 2 shows exemplary seismocardiogram (SCG) and photoplethysmogram(PPG) waveforms obtained by the methods described herein, depictingexemplary fiducial points within the synchronized waveformscorresponding to vascular transit time (VTT).

FIG. 3 shows an exemplary seismocardiogram (SCG) which has beenband-pass filtered to 5 Hz-35 Hz and signal averaged within a timewindow, and a corresponding transformation of this waveform by slope sumtransformation.

FIG. 4 shows exemplary electrocardiogram (ECG) and photoplethysmogram(PPG) waveforms obtained by the methods described herein, depictingexemplary fiducial points within the synchronized waveformscorresponding to pulse arrival time (PAT).

DETAILED DESCRIPTION OF THE INVENTION

Measurement Overview

The methods and systems described herein provide for continuousnoninvasive measurement of vital signs such as blood pressure (cNIBP)based on PAT, which features a number of improvements over conventionalPAT measurements. The invention uses a body-worn monitor thatrecursively determines an estimated PEP for use in correcting PATmeasurements by detecting low frequency vibrations created during acardiac cycle, and using a state estimator algorithm to identify signalsindicative of aortic valve opening in those measured vibrations. Asdescribed below in an exemplary embodiment, a real-time cNIBP algorithmis described that utilizes a moving window to estimate blood pressure bycombining timing measurements derived from ECG, SCG, and PPG waveforms.These timing measurements include PAT, PEP, and VTT. In theseembodiments, a moving window is used to process the waveform data andprovide continuous updates each cardiac cycle rather than batchprocessing the measurements from these waveforms.

Fiducial points derived from the SCG waveform are identified and used toadaptively compensate for changes in PEP that corrupt the PATmeasurements used in the cNIBP algorithm. Signal averaging of the mostrecent cardiac cycle from the SCG waveform with multiple previous SCGcardiac cycles permit the algorithms to remove in-band noise and improvethe distinction of physiologic features in the waveform. The SCG has avery poor signal-to-noise ratio (SNR) in some individuals, so thissignal averaging the SCG provides an efficient way to significantlyimprove the SNR and the precision of PEP measurements

As described hereinafter, applying a slope sum transformation to thesignal averaged SCG waveform can help distinguish and select fiducialpoints in the current SCG waveform. The fiducial points include mitralvalve closure, aortic valve opening, and aortic valve closure.Additionally, signal quality metrics (SQI's) derived from the SCGwaveform and chest accelerometer are used to remove corrupted data fromthe SCG waveform prior to signal averaging the SCG waveform acrossmultiple cardiac cycles. This reduces errors caused by motion artifactand minimizes variance in the measured PEP.

Similarly, SQI's derived from the PPG waveform and wrist accelerometercan be used to removed corrupted data values out of the calculation of amedian PAT (or VTT) value. This reduces errors caused by motion artifactand minimizes variance in the measured PAT. Removing such corruptedwaveform data prior to the median calculation is dramatically improvesthe measurement of cNIBP in a body worn device.

The present methods rely on use of a state estimator algorithm(exemplified as an adaptive Kalman filter) to determine improved meanand/or median PEP and PAT measurements that weights the update rate ofthe latest measurement based on the variance of the measurements in amoving time window. This update rate of the measurement can be fast whenthe variance in the moving window is small and slow when the variance inthe moving window is large. This provides an efficient mechanism thatallows cNIBP to react slowly or quickly to changes in PAT and/or VTTbased on the variance.

Sensor Configurations

FIG. 1 depicts the system described herein as a cNIBP monitor consistingof an ECG/accelerometer module 101, a wrist transceiver/processing unit104, a pulse oximetry module 105 and NIBP module 108 which determines anoscillometric blood pressure measurement. These device components arecapable of measuring four different physiologic signals; anelectrocardiogram (ECG), a photoplethysmogram (PPG), a vibrationalwaveform known as a seismocardiogram (SCG), and a brachial arterypressure signal that provides an oscillometric blood pressuremeasurement (NIBP).

The exemplified system comprises an ECG/accelerometer sensor module 101that includes a housing enclosing (i) an ECG circuit operably connectedto a transceiver within the housing that transmits ECG waveforms (e.g.,using cable 106 or by wireless connection) to a correspondingtransceiver housed within a processing apparatus 104; and (ii) anaccelerometer (e.g., ADXL-345 or LSM330D) also operably connected to thetransceiver within the housing that transmits accelerometer (SCG)waveforms to a corresponding transceiver housed within a processingapparatus 104. ECG/accelerometer sensor module 101 is positioned on thepatient's skin at the sternum. While the ECG sensor module and theaccelerometer module may be provided separately, it is advantageous forease of use that a single housing 101 encloses both sensor modules.Similarly, while the processing apparatus 104 is depicted herein as asingle body-worn processor unit, the algorithms described herein may beperformed by a plurality of processors which may be housed at differentlocations, each of which contributes to the processing power of thesystem, and which are collectively therefore referred to as “theprocessing apparatus.” By way of example only, a processing unit may beprovided at the bedside or provided in a body-worn client/remote serverprocessor format.

In order to achieve a sufficient signal-to-noise ratio for the SCGsignal the ECG/accelerometer module 101 should be mechanically coupledto the patient's skin. The housing of the ECG/accelerometer module 101is secured against the patient's skin using a double-sided adhesivesubstance applied directly between the housing and the skin or bysnapping it into a rigid fixture that is adhered to the skin. Thehousing should be attached at the sternum of the patient, optimally thelower sternum just above the xiphoid process. The microprocessorcomponent of transceiver/processing apparatus 104 applies algorithms asdescribed below in order to collectively process ECG waveforms alongwith SCG waveforms to generate an improved PAT measurement.

The ECG circuit within the ECG/accelerometer module 101 features asingle circuit (e.g. an ASIC) that collects electrical signals from aseries of body-worn electrodes 102 and coverts these signals into adigital ECG waveform. Such a circuit connects to the wrist-worntransceiver through a digital, serial interface (e.g. an interface basedon a “controller area network”, or “CAN”, system). The chest-wornECG/accelerometer module 101 connects through cables 108 to conventionalECG electrodes 102 located, respectively, in the upper right-hand, upperleft-hand, and lower left-hand portions of the patient's thorax. Threeelectrodes 102 (two detecting positive and negative signals, and oneserving as a ground) are typically required to detect the necessarysignals to generate an ECG waveform with an adequate signal-to-noiseratio. RED DOT™ electrodes marketed by 3M (3M Center, St. Paul, Minn.55144-1000) are suitable for this purpose. During a measurement, the ECGelectrodes 102 measure analog signals that pass to circuits within theECG/accelerometer module 101. There, ECG waveforms are generated,digitized (typically with 12-24-bit resolution and a sampling ratebetween 250-500 Hz), and formulated in individual packets so they can betransmitted to the wrist-worn transceiver/processing apparatus 104 forprocessing.

The individual packets described above may be preferably transmittedaccording to the controller area network (CAN) protocol. Use of thisprotocol with a wired or wireless connection between theECG/accelerometer module 101 and wrist-worn transceiver/processingapparatus 104 provides packets in which all timing related informationbetween the packets is preserved such that the waveforms generated bythe ECG and accelerometer may be synchronized (optionally with PPGwaveforms) by the wrist-worn transceiver/processing apparatus 104. TheCAN protocol also permits the data corresponding to waveforms generatedby the ECG and accelerometer to be segregated although transmitted by asingle transceiver, as each packet can contain information designatingthe sensor from which the data originates.

The optical sensor 105 detects optical radiation modulated by theheartbeat-induced pressure wave, which is further processed with asecond amplifier/filter circuit within the transceiver/processingapparatus 104. This results in the PPG waveform, which, as describedabove, includes a series of pulses, each corresponding to an individualheartbeat. The depicted thumb-worn optical sensor 105 is operablyconnected (wirelessly or through a cable 109 to the wrist-worntransceiver/processing apparatus 104 to measure and transmit PPGwaveforms that, when combined with the ECG waveform, can be used togenerate cNIBP measurements according to the algorithms described below.This yields individual blood pressure values (systolic or “SYS”,diastolic or “DIA”, and mean arterial or “MAP”). The optical sensor 105additionally measures a PPG waveform that can be processed to determineSpO2 values, as described in detail in the following patent application,the contents of which are incorporated herein by reference: BODY-WORNPULSE OXIMETER, U.S. Ser. No. 12/559,379, filed Sep. 14, 2009.

In addition to the accelerometer located on the sternum within housing101, the system comprises two other accelerometers; one positioned onthe wrist within the wrist-worn transceiver/processing apparatus 104 andthe other on the upper arm of the same arm 103. Each measures threeunique signals, each corresponding to the x, y, and z-axes of the bodyportion to which the accelerometer attaches. These signals are thenprocessed by the wrist-worn transceiver/processing apparatus 104 with aseries of algorithms, some of which are described in the followingpatent application, the contents of which are incorporated herein byreference: BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING ANDANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009) todetermine motion, posture, arm height, and activity level.

Finally, the system further comprises a pneumatic cuff-based module 108that communicates with the wrist-worn transceiver/processing apparatus104 in order to obtain oscillometric NIBP measurements. The cuff module108 features a pneumatic system that includes a pump, valve, pressurefittings, pressure sensor, analog-to-digital converter, microcontroller,transceiver, and rechargeable Li:ion battery. During an indexingmeasurement, the pneumatic system inflates a disposable cuff andperforms two measurements: 1) an inflation-based measurement ofoscillometry to determine values for SYS_(INDEX), DIA_(INDEX), andMAP_(INDEX); and 2) a patient-specific slope describing the relationshipbetween PTT and MAP. These measurements are described in detail in theabove-referenced patent application entitled: ‘VITAL SIGN MONITOR FORMEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSUREWAVEFORMS’ (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contentsof which have been previously incorporated herein by reference. Pressurewaveforms are transmitted by the transceiver to the wrist-worntransceiver/processing apparatus 104 (wirelessly or through cable 107)through a digital, serial interface, and preferably as packets accordingto the controller area network (CAN) protocol.

Summary of the Data Acquisition and Signal Filtering

ECG: Electrocardiogram signals may be sampled at a sampling frequency of500 Hz. The ECG signals were digitally filtered using a high pass filter(−3 dB at 0.7 Hz) and a 60 Hz notch filter, digitized using an A/Dconverter contained within the ECG/accelerometer module 101, andtransmitted as packet data to the wrist-worn transceiver/processingapparatus 104.

PPG: The photoplethysmogram signals were sampled by an analog to digitalconverter at a sampling frequency of 500 Hz. The PPG signals weredigitally filtered using a low-pass filter (−3 dB at 10 Hz) andtransmitted as analog data to the wrist-worn transceiver/processingapparatus 104 where the signals are digitized by an A/D converter withinthe wrist-worn transceiver/processing apparatus 104.

NIBP: The pressure transducer signals were sampled by an analog todigital converter at a sampling frequency of 500 Hz. The pressuresignals were digitally filtered using a low-pass filter (−3 dB at 6 Hz),digitized using an A/D converter contained within the cuff module 108,and transmitted as packet data to the wrist-worn transceiver/processingapparatus 104.

SCG: The seismocardiogram signals were digitally captured at a samplingfrequency of 500 Hz. The SCG signals were digitally filtered using aband-pass filter (−3 dB at 5 Hz and 35 Hz), digitized using an A/Dconverter contained within the ECG/accelerometer module 101, andtransmitted as packet data to the wrist-worn transceiver/processingapparatus 104.

Measurement of PAT & VTT

The cNIBP measurement is based on the measurement of pulse wavevelocity. The velocity of a pressure pulse as it travels along anarterial pathway is dependent on the distensibility of the arteriesalong the transit path. Distensibility is a function of the complianceand volume of the artery. The distensibility of an artery is dependenton the pressure acting across the arterial wall or transmural pressure.Typically the transmural pressure is equivalent to the arterial bloodpressure hence the velocity of the pulse wave is a function of arterialpressure. Two different timing measurements, vascular transit time (VTT)and pulse arrival time (PAT) were used to quantify the velocity of thepressure pulse wave.

VTT was measured on a beat-to-beat basis as the time difference betweenthe onset of the photoplethysmogram at the base of the thumb (or finger)and the opening of the aortic valve measured by the accelerometer basedseismocardiogram as shown in FIG. 2. The fiducial point used torepresent the opening of the aortic valve in the SCG waveform wasdetermined in the following manner.

Signal averaging: The SCG signal may contain noise within the frequencyband of our band-pass filter (5 Hz-35 Hz). A signal averaging techniquecan be used to remove this noise prior to locating aortic valve opening.The SCG signal can be viewed as the response to the mechanical activityof the heart during each cardiac cycle. The duration of a cardiac cyclecan be defined as the time between adjacent peaks of the QRS cycle inthe ECG or RR interval. The signal averaged SCG waveform correspondingto each cardiac cycle is defined using the sample numbers of the R-peaks(R_(peak)[i]) as given in equation (1). The length variable L,corresponds to the median number of samples in the RR intervals withinthe last N cardiac cycles.

$\begin{matrix}{{{SCG}\left\lbrack {{R_{peak}\lbrack i\rbrack} + j} \right\rbrack} = {{\frac{1}{N}{\sum\limits_{k = {i - N - 1}}^{i}{SC{G\left\lbrack {{R_{peak}\lbrack k\rbrack} + j} \right\rbrack}\mspace{14mu} {for}\mspace{14mu} 0}}} \leq j < L}} & (1)\end{matrix}$

Waveform transformation: In order to accentuate features in the filteredSCG waveform for automated detection every sample of the filtered SCGwaveform for each cardiac cycle is transformed into a slope sum waveform(SSF) as given in equations (2), (3), and (4) where length M is selectedto accentuate the features corresponding to the opening of the aorticvalve.

$\begin{matrix}{\frac{dSC{G\lbrack i\rbrack}}{dt} = {{SC{G\lbrack i\rbrack}} - {SC{G\left\lbrack {i - 1} \right\rbrack}}}} & (2) \\{{{{if}\mspace{14mu} \frac{dSC{G\lbrack i\rbrack}}{dt}} > {0\mspace{14mu} {then}\mspace{14mu} \frac{dSC{G\lbrack i\rbrack}}{dt}}} = 0} & (3) \\{{{SSF}\lbrack i\rbrack} = {\sum\limits_{i - M}^{i}\frac{dSC{G\lbrack i\rbrack}}{dt}}} & (4)\end{matrix}$

The two negative peaks with largest absolute magnitude in the SSFwaveform for each cardiac cycle are identified using an adaptivethreshold to eliminate noise and other small magnitude vibrations.

The peak of the filtered SCG waveform that lies between the two negativeslope sum peaks was determined as the fiducial point that defined theopening of the aortic valve.

The VTT value was considered to be valid and used by the blood pressurealgorithm if the delay between the SCG fiducial point and the peak ofthe ECG QRS complex fell within a specified range [75 ms 175 ms], and ifthe VTT value was within a valid range [30 ms 300 ms].

The filtered SCG waveform and slope sum waveform are shown in FIG. 3along with the adaptive threshold and the fiducial points selected torepresent the opening of the aortic valve.

PAT was measured on a beat-to-beat basis as the time difference betweenthe onset of the photoplethysmogram at the base of the thumb (or indexfinger) and the peak of the QRS complex in the ECG waveform as shown inFIG. 4.

Blood Pressure Estimation with Vascular Transit Time

Continuous blood pressure measurements were determined directly from thevascular transit time measurements calculated from the seismocardiogramand photoplethysmogram. A summary of the method is provided below.

Step 1) Calibrate VTT to Blood Pressure with the NIBP Cuff Inflation

In order to calculate blood pressure using VTT a model was identified tocharacterize the relationship between VTT and mean arterial bloodpressure (MAP) as given in equation (5) where L_(t) represents thelength of the arterial transit path between the aortic valve and thesite of the optical sensor.

$\begin{matrix}{{VTT} = {\left( \frac{L_{t}}{pwv} \right) = \left( \frac{L_{t}}{{aMAP} + {pwv_{0}}} \right)}} & (5)\end{matrix}$

The model parameters (a and pwv₀) were identified using the NIBP moduleto create a unique calibration for each subject.

During cuff inflation the increase in cuff pressure (P_(cuff)) causes adecrease in the transmural pressure (P_(tm)) acting across the brachialartery wall. An expression for the transmural pressure acting on thearterial segment under the cuff is given in equation (6) where theintravascular pressure is defined as the mean arterial pressure at thetime of the inflation (MAP_(cal)).

P _(tm)(t)=MAP _(cal) −P _(cuff)(t)  (6)

As the transmural pressure acting on the brachial artery decreases withan increase in cuff pressure the compliance and distensibility of theartery increases. The increased distensibility decreases the velocity ofthe pressure pulse in this arterial segment thereby increasing themeasured VTT. The expression used to describe the relationship betweenVTT, the mean arterial pressure measured by the NIBP module cal,(MAP_(cal)), and the pressure in the NIBP cuff (P_(cuff)) duringinflation is given in equation (7) where L_(e) represents the length ofthe brachial artery affected by the cuff.

$\begin{matrix}{{{VTT}(t)} = {\left( \frac{L_{t} - L_{c}}{{aMAP_{cal}} + {pwv_{0}}} \right) + \left( \frac{L_{c}}{{a\left( {{MAP_{cal}} - {P_{cuff}(t)}} \right)} + {pwv_{0}}} \right)}} & (7)\end{matrix}$

The unknown parameter values (a and pwv₀) are identified from the VTTvalues and cuff pressures measured during the NIBP inflation period byminimizing the squared error between the measured and estimated VTTusing the Levenberg-Marquardt algorithm.

Additionally, inflation of the NIBP cuff provides an oscillometric bloodpressure measurement for systolic pressure (SYS_(cal)), diastolicpressure (DIA_(cal)), and mean arterial pressure (MAP_(cal)) at the timeof the inflation.

The unique patient ratio used to estimate diastolic pressure (R_(dia))in the cNIBP algorithm is calculated based on the oscillometric bloodpressure measurements as given in equation (8).

$\begin{matrix}{R_{dia} = \left( \frac{{DIA}_{cal}}{MAP_{cal}} \right)} & (8)\end{matrix}$

The unique patient ratio used to estimate systolic pressure (R_(sys)) inthe cNIBP algorithm is calculated based on the oscillometric bloodpressure measurements as given in equation (9).

$\begin{matrix}{R_{sys} = \left( \frac{{SYS}_{cal}}{MAP_{cal}} \right)} & (9)\end{matrix}$

Step 2) Calculate an Aggregate VTT

Vascular transit time values are measured on a beat-by-beat basishowever signal artifact in the SCG and PPG due to motion or othersources of corruption will cause significant error in these real-timemeasurements particularly for a patient worn monitor. Additionally,physiologic phenomenon such as thoracic pressure change due torespiration cause variation in VTT on a beat-by-beat basis. The cNIBPdetermination employees a multi-step algorithm to remove spurious VTTmeasurements and provide an aggregated VTT value to calculate anaccurate blood pressure.

Beat-to-beat VTT values were classified as rejected or accepted based onseveral screening criteria. A VTT value was rejected if a patient'smotion exceeded a fixed threshold. The magnitude of patient motion wasmeasured by accelerometers located in the wrist transceiver and ECGmodule. Additionally VTT values were classified using several signalquality indices (SQI's) derived from the PPG signal. The PPG SQI's weredetermined from the PPG pulse used to identify the onset point for theVTT measurement. A VTT value was classified as rejected if the SQI'sexhibited variance above a predefined threshold. The VTT values werealso classified using several SQI's derived from the SCG signal. TheSQI's were determined from the SCG signal for the cardiac cycle used todetermine when the aortic valve opened. A VTT value was classified asrejected when the SQI's exhibited variance above a predefined threshold.

An aggregate VTT value was calculated periodically (every 3 seconds)from all of the VTT measurements collected in the 60-second period priorto the update time.

The first step used to determine an aggregate VTT value was to calculatea mean VTT value (m₁) and the standard deviation (σ₁) of the VTT valuesin the 60-second window for all measurements that were classified asaccepted.

The second step in the process was to calculate a median VTT value(MED_(VTT)) and the variance of the VTT values (σ² _(VTT)) in the60-second window using VTT measurements that were screened as acceptedand VTT values that were inside an upper bound (m₁+2σ₁) and lower bound(m₁−2σ₁) defined using the mean and standard deviation calculated instep 1 of the VTT aggregation.

The final step used to determine an aggregate VTT value for eachperiodic update was to apply an adaptive moving average filter to themedian VTT value from step 2 and the previous aggregated VTT value. Theadaptive filter utilizes the framework of a Kalman filter to calculate again that was applied to the difference between the new median VTT valueand the previous aggregated VTT value. The Kalman filter allows us toincrease the response rate of the cNIBP monitor to a change in VTT whenour confidence in the new median value is high and decreases the cNIBPresponse rate to VTT change when our confidence in the new median valueis low. The procedure used to determine the aggregated VTT numeric value(VTT_(num)) with the adaptive filter is given below.

Calculate the a priori error covariance (P_(pri)) given the previousestimate or an initial estimate of the posteriori error variance(P_(post)), the VTT variance calculated in step 2, and the statetransition matrix A.

P _(pri) =A·P _(post) ·A ^(T)+σ_(VTT) ²  (10)

Calculate the Kalman Gain (G) given the output matrix (H) and apre-defined PAT process variance (R_(VTT)).

G=P _(pri) ·H ^(T)·(H·P _(pri) ·H ^(T) +R _(VTT))⁻¹  (11)

Calculate the a priori state estimate (x_(pri)) based on the posterioristate estimate (x_(post)) and state transition matrix A.

x _(pri) =A·x _(post)  (12)

Update the posteriori state estimate using the median VTT value and theKalman gain (G).

x _(post) =x _(pri) +G·(MED _(VTT) −H·x _(pri))  (13)

The aggregate VTT numeric (VTT_(num)) value can be defined in terms ofthe output matrix (H) and the posteriori state estimate (x_(post))

VTT _(num) =H·x _(post)  (14)

Prior to the next periodic calculation the error covariance (P_(post))was updated.

P _(post)=(I−G·H)·P _(pri)  (15)

The simplest implementation of this filter is one that defines A=H=1. Inthis case the implementation can be reduced to 3 equations.

G=(P _(post)+σ_(VTT) ²)/(P _(post)+σ_(VTT) ² +R _(VTT))  (16)

VTT _(num)(t)=VTT _(num)(t−3 sec)+G·(VTT _(num)(t−3 sec)−VTT_(med))  (16)

P _(post)=(1−G)·(P _(post)+σ_(VTT) ²)  (17)

Step 3) Calculate Blood Pressure

Values for systolic, diastolic, and mean arterial pressure aredetermined for every aggregate VTT value calculated in the previous stepusing the formulas given in equations (18), (19), and (20) whereVTT_(cal) represents an aggregate VTT measured at the time of the NIBPinflation.

$\begin{matrix}{{MAP} = {{\left( \frac{L_{t}}{a} \right)\left( {\frac{1}{VTT_{num}} - \frac{1}{VTT_{cal}}} \right)} + {MAP_{cal}}}} & (18) \\{{SYS} = {R_{sys} \cdot {MAP}}} & (19) \\{{DIA} = {R_{dia} \cdot {MAP}}} & (20)\end{matrix}$

Correction for Pre-Ejection Period Changes Using the Seismocardiogram

In addition to providing a measure of vascular transit time the SCGsignal can be used to measure the duration of the cardiac pre-ejectionperiod. The pre-ejection period (PEP) is the combination of theelectromechanical delay of the heart and the period of isovolumiccontraction of the left ventricle prior to the opening of the aorticvalve. PEP can be measured as the time difference between the opening ofthe aortic valve in the SCG waveform and the peak of the QRS cycle inthe ECG waveform.

Pulse arrival time is the time difference between the onset of the PPGwaveform and the peak of the QRS cycle in the ECG. Due to the ECG's highsignal to noise ratio and its motion tolerance it can provide a morerobust means to determine beat to beat changes in pulse wave velocitycompared to using the SCG to calculate VTT. Therefore continuousnon-invasive blood pressure can be calculated directly from pulsearrival time.

However, in addition to capturing changes in pulse wave velocity the PATvalues also track changes in PEP that may be uncorrelated with changesin arterial blood pressure. The PEP measured by the SCG waveform can beused to identify and compensate for changes in PAT that are induced bychanges in PEP. This strategy allows PEP values measure from an SCGsignal with poor signal to noise ratio to use a longer averaging windowwithout sacrificing the cNIBP monitors response to changes in arterialpressure measured with PAT.

The procedure used to make cNIBP measurements with PAT while correctingfor changes in PEP using an accelerometer-based seismocardiogram isoutlined below.

Step 1) Calibrate PAT to Blood Pressure with the NIBP Cuff Inflation

The algorithm used to identify a calibration equation between PAT andMAP using the NIBP inflation is analogous to the procedure described forVTT in the previous section except that VTT measurements are replacedwith PAT measurements as given in equation (21).

$\begin{matrix}{{PAT} = {\left( \frac{L_{t}}{pwv} \right) = \left( \frac{L_{t}}{{aMAP} + {pwv_{0}}} \right)}} & (21)\end{matrix}$

The model parameters (a and pwv₀) were identified using the NIBP moduleto create a unique calibration for each subject.

Step 2) Calculate an Aggregate PAT, PEP, and cPAT

PAT values and PEP values are measured on a beat-by-beat basis howeversignal artifact in the ECG, PPG, and SCG due to motion or other sourcesof corruption will cause significant error in these real-timemeasurements particularly for a patient worn monitor. The ViSi cNIBPmonitor employees a multi-step algorithm to remove spurious PAT and PEPmeasurements and provide aggregated PAT and PEP values to calculateblood pressure.

Beat-to-beat PAT values were classified as rejected or accepted based onseveral screening criteria. A PAT value was rejected if a patient'smotion exceeded a fixed threshold. The magnitude of patient motion wasmeasured by an accelerometer located in the wrist transceiver.Additionally PAT values were classified using several SQI's derived fromthe PPG signal. The SQI's were determined from the PPG pulse used toidentify the onset point. When the SQI's exhibited variance above apredefined threshold the PAT value was classified as rejected if it wasbelow the threshold it was accepted.

Beat-to-beat PEP values were also classified as rejected or acceptedbased on several screening criteria. A PEP value was rejected if apatient's motion exceeded a fixed threshold. The magnitude of patientmotion was measured by an accelerometer located in the ECG module.Additionally PEP values were classified using several SQI's derived fromthe SCG signal. The SQI's were determined from the SCG signal for thecardiac cycle used to determine when the aortic valve opened. When theSQI's exhibit variance above a predefined threshold the PEP value wasclassified as rejected if it was below the threshold it was accepted.

An aggregate PAT value was calculated periodically (every 3 seconds)from all of the PAT measurements collected in the 60-second period priorto the update time. The first step used to determine an aggregate PATvalue was to calculate a mean PAT value (m₁) and the standard deviation(σ₁) of the PAT values in the 60-second window for all measurements thatwere classified as accepted.

The second step in the process was to calculate a median PAT value(PAT_(me)d) and the variance of the PAT values (σ² _(PAT)) in the60-second window using PAT measurements that were screened as acceptedand PAT values that were inside an upper bound (m₁+2σ₁) and lower bound(m₁−2σ₁) defined using the mean and standard deviation calculated instep 1 of the PAT aggregation.

The final step used to determine an aggregate PAT value for eachperiodic update was to apply an adaptive moving average filter to themedian PAT value from step 2 and the previous aggregated PAT value. Theadaptive filter utilizes the framework of a Kalman filter to calculate again that was applied to the difference between the new median PAT valueand the previous aggregated PAT value. The Kalman filter allows us toincrease the response rate of the cNIBP monitor to a change in PAT whenour confidence in the new median value is high and decrease the cNIBPresponse rate to PAT change when our confidence in the new median valueis low. The procedure used to determine the aggregated PAT numeric value(PAT_(num)) with the adaptive filter is given below.

Calculate the a priori error covariance (P_(pri)) given the previousestimate or an initial estimate of the posteriori error variance(P_(post)), the PAT variance calculated in step 2, and the statetransition matrix A.

P _(pri) =A·P _(post) ·A ^(T)+σ_(PAT) ²  (22)

Calculate the Kalman Gain (G) given the output matrix (H) and apre-defined PAT process variance (R_(PAT)).

G=P _(pri) ·H ^(T)·(H·P _(pri) ·H ^(T) +R _(PAT))⁻¹  (23)

Calculate the a priori state estimate (x_(pri)) based on the posterioristate estimate (x_(post)) and state transition matrix A.

x _(pri) =A·x _(post)  (24)

Update the posteriori state estimate using the median PAT value and theKalman gain (G).

x _(post) =x _(pri) +G·(PAT _(med) −H·x _(pri))  (25)

The aggregate PAT numeric (PAT_(num)) value can be defined in terms ofthe output matrix (H) and the posteriori state estimate (x_(post))

PAT _(num) =H·x _(post)  (26)

Prior to the next periodic calculation the error covariance (P_(post))was updated.

P _(post)=(I−G·H)·P _(pri)  (27)

The simplest implementation of this filter is one that defines A=H=1. Inthis case the implementation can be reduced to 3 equations.

G=(P _(post)+σ_(PAT) ²)/(P _(post)+σ_(PAT) ² R _(PAT))  (28)

PAT _(num)(t)=PAT _(num)(t−3 sec)+G·(PAT _(num)(t−3 sec)−PAT_(med))  (29)

P _(post)=(1−G)·(P _(post)+σ_(PAT) ²)  (30)

An aggregate PEP value was calculated periodically (every 3 seconds)from all of the PEP measurements collected in the 60-second period priorto the time of the update.

The first step used to determine an aggregate PEP value was to calculatea mean PEP value (m₁) and the standard deviation (σ₁) of the PEP valuesin the 60-second window for all measurements that were classified asaccepted.

The second step in the process was to calculate a median PEP value(PEP_(med)) and the variance of the PEP values (σ² _(PEP)) in the60-second window using PEP measurements that were screened as acceptedand PEP values that were inside an upper bound (m₁+2σ₁) and lower bound(m₁−2σ₁) defined using the mean and standard deviation calculated instep 1 of the PEP aggregation.

The final step used to determine an aggregate PEP value for eachperiodic update was to apply an adaptive moving average filter to themedian PEP value from step 2 and the aggregated PEP value from the prior3-second update. The adaptive filter utilizes the framework of a Kalmanfilter to calculate a gain that was applied to the difference betweenthe new median PEP value and the previous aggregated PEP value. TheKalman filter allows us to increase the response rate of the PEPmeasurement when our confidence in the new median is high and decreasesthe response rate to PEP change when our confidence in the new medianvalue is low. The procedure used to determine the aggregated PEP numericvalue (PEP_(num)) with the adaptive filter is given below.

Calculate the a priori error covariance (P_(pri)) given the previousestimate or an initial estimate of the posteriori error variance(P_(post)), the PEP variance calculated in step 2, and the statetransition matrix A.

P _(pri) =A·P _(post) ·A ^(T)+σ_(PEP) ²  (31)

Calculate the Kalman Gain (G) given the output matrix (H) and apre-defined PEP process variance (R_(PEP)).

G=P _(pri) ·H ^(T)·(H·P _(pri) ·H ^(T) +R _(PEP))⁻¹  (32)

Calculate the a priori state estimate (x_(pri)) based on the posterioristate estimate (x_(post)) and state transition matrix A.

x _(pri) =A·x _(post)  (33)

Update the posteriori state estimate using the median PEP value and theKalman gain (G).

x _(post) =x _(pri) +G·(PEP _(med) −H·x _(pri))  (34)

The aggregate PEP numeric (PEP_(num)) value can be defined in terms ofthe output matrix (H) and the posteriori state estimate (x_(post))

PEP _(num) =H·x _(post)  (35)

Prior to the next periodic calculation update the error covariance(P_(post)).

P _(post)=(I−G·H)·P _(pri)  (36)

A simple implementation of the adaptive filter is one that definesA=H=1. In this case the implementation can be reduced to 3 equationsgiven below.

G=(P _(post)+σ_(PEP) ²)/(P _(post)+σ_(PEP) ² +R _(PEP))  (37)

PEP _(num)(t)=PEP _(num)(t−3 sec)+G·(PEP _(num)(t−3 sec)−PEP_(med))  (38)

P _(post)=(1−G)·(P _(post)+σ_(PEP) ²)  (39)

Step 3) Calculate Blood Pressure

The first step to determine a blood pressure value is to calculate acorrected PAT value (cPAT) using the latest aggregated PAT and PEPvalues as given in equation (40) where PEP_(cal) represents an aggregatePEP measured at the time of the NIBP inflation.

cPAT=PAT _(num)−(PEP _(num) −PEP _(cal))  (40)

Values for systolic, diastolic, and mean arterial pressure aredetermined for every cPAT value using the formulas given in equations(41), (42), and (43) where PAT_(cal) represents an aggregate PATmeasured at the time of the NIBP inflation.

$\begin{matrix}{{MAP} = {{\left( \frac{L_{t}}{a} \right)\left( {\frac{1}{cPAT} - \frac{1}{PAT_{cal}}} \right)} + {MAP_{cal}}}} & (41) \\{{SYS} = {R_{sys} \cdot {MAP}}} & (42) \\{{DIA} = {R_{dia} \cdot {MAP}}} & (43)\end{matrix}$

One skilled in the art readily appreciates that the present invention iswell adapted to carry out the objects and obtain the ends and advantagesmentioned, as well as those inherent therein. The examples providedherein are representative of preferred embodiments, are exemplary, andare not intended as limitations on the scope of the invention.

It will be readily apparent to a person skilled in the art that varyingsubstitutions and modifications may be made to the invention disclosedherein without departing from the scope and spirit of the invention.

All patents and publications mentioned in the specification areindicative of the levels of those of ordinary skill in the art to whichthe invention pertains. All patents and publications are hereinincorporated by reference to the same extent as if each individualpublication was specifically and individually indicated to beincorporated by reference.

The invention illustratively described herein suitably may be practicedin the absence of any element or elements, limitation or limitationswhich is not specifically disclosed herein. Thus, for example, in eachinstance herein any of the terms “comprising”, “consisting essentiallyof” and “consisting of” may be replaced with either of the other twoterms. The terms and expressions which have been employed are used asterms of description and not of limitation, and there is no intentionthat in the use of such terms and expressions of excluding anyequivalents of the features shown and described or portions thereof, butit is recognized that various modifications are possible within thescope of the invention claimed. Thus, it should be understood thatalthough the present invention has been specifically disclosed bypreferred embodiments and optional features, modification and variationof the concepts herein disclosed may be resorted to by those skilled inthe art, and that such modifications and variations are considered to bewithin the scope of this invention as defined by the appended claims.

Other embodiments are set forth within the following claims.

1-30. (canceled)
 31. A system for obtaining a continuous measurement ofcardiac pre-ejection period for an individual, comprising: an ECG sensorconfigured to be worn on the patient's body and detect signalsindicative of electrical activity of the individual's heart for time n,filter the signals using a 60 Hz notch filter, digitize the filteredsignals, generate therefrom a time-dependent electrocardiogram waveform,and transmit the time-dependent electrocardiogram waveform as packetdata; a seismocardiogram sensor configured to be worn on the patient'sbody and detect signals indicative of compression waves produced due tocontraction of the individual's heart for time n, filter the signalsusing a 5 and 35 Hz bandpass filter, digitize the filtered signals,generate therefrom a time-dependent seismocardiogram waveform, transmitthe time-dependent seismocardiogram waveform as packet data; and aprocessing apparatus operably connected to the first body-worn sensorand the second body-worn sensor, the processing apparatus configured toreceive the time-dependent electrocardiogram waveform transmitted by theECG sensor and the time-dependent seismocardiogram waveform transmittedby the seismocardiogram sensor, and to (i) recursively determine theoccurrence of aortic valve opening for each contraction c of theindividual's heart during time n by processing a portion of thetime-dependent seismocardiogram waveform within a moving time window oflength l comprising a plurality of contractions of the individual'sheart which includes contraction c to provide an averagedseismocardiogram waveform corresponding to contraction c, andidentifying a first fiducial point indicating opening of the aorticvalve for contraction c in the averaged seismocardiogram waveform, and(ii) recursively determine a pre-ejection period (PEP) for eachcontraction c of the individual's heart during time n by determining atime difference between a second fiducial point in the time-dependentelectrocardiogram waveform indicating the onset of electricalstimulation of the ventricles during contraction c and the firstfiducial point indicating opening of the aortic valve for contraction c.32. A system according to claim 31, wherein the processing apparatus isfurther configured to recursively determine an estimated time of aorticvalve closure for each contraction c of the individual's heart duringtime n.
 33. A system according to claim 31, wherein the processingapparatus is further configured to recursively determine an estimatedtime of mitral valve closure for each contraction c of the individual'sheart during time n.
 34. A system according to claim 31, wherein time nis at least one hour.
 35. A system according to claim 31, wherein thelength of the moving window is between 15 seconds and 2 minutes.
 36. Asystem according to claim 31, further comprising: a plethysmogram sensorapparatus configured to be worn on the patient's body and detect signalsindicative of changes in blood volume at an extremity produced due tocontraction of the individual's heart for time n and generate therefroma time-dependent plethysmogram waveform.
 37. A system according to claim36, wherein the plethysmogram sensor apparatus is configured to be wornat the base of the patient's thumb or finger.
 38. A system according toclaim 37, wherein the processing apparatus is operably connected to thethird body-worn sensor and is configured to recursively determine aPulse Arrival Time (PAT) for each contraction c of the individual'sheart during time n by determining a time difference between the secondfiducial point in the time-dependent electrocardiogram waveformindicating the onset of electrical stimulation of the ventricles duringcontraction c and a third fiducial point in the time-dependentplethysmogram waveform indicating arrival of the pressure wave at theextremity due to contraction c.